Assessing Implicit Evaluative Attitudes Toward Violence in Male Students, Male Community Members, and Men Convicted of Violent Offences
Bibliographic record
Abstract
According to theory and research, evaluative attitudes are considered a central and precipitating factor in behaviour; however, this construct appears less focussed on within the violence literature.Evaluative attitudes can be classified as explicit (assessed with self-report scales) and implicit, often assessed using response latency measures.Some studies have reported a link between implicit evaluative attitudes toward violence, violent behaviour, and risk relevant constructs, whereas others have reported no link.The aim of this dissertation was to modify existing implicit procedures to assess implicit evaluative attitudes toward violence alongside other commonly assessed violent cognitions.Three response latency measures were administered: a traditional Implicit Association Test (IAT), Personalized IAT, and Relational Responding Task.A Pre-test was conducted to determine the optimal categories and stimuli to use for the implicit measures (N = 207 online adult males).The relationships between implicit evaluative attitudes toward violence, explicit evaluative attitudes toward violence, beliefs regarding violence, and violent behaviour were assessed among three samples of adult males: undergraduate students (N = 156) and community members (N = 95; Study 1), men convicted of violent offences (N = 33; Study 2), and community members recruited online (N = 627 and 820; Study 3).Overall, implicit evaluative attitudes toward violence were more strongly related to beliefs regarding violence than explicit evaluative attitudes toward violence.More positive implicit evaluative attitudes toward violence were consistently related to greater likelihood of violence.Explicit evaluative attitudes toward violence and beliefs regarding violence were moderately to strongly associated with one another, as well as violent outcomes.Using regression analyses, implicit evaluative attitudes toward IMPLICIT EVALUATIVE ATTITUDES TOWARD VIOLENCE iii violence consistently explained additional variance in likelihood of violence over explicit/self-report violent cognitions, as well as moderated the relationships between explicit/self-report violent cognitions and some violent outcomes (e.g., likelihood in Studies 1 and 3 and violence risk in Study 2).In Study 3, results from an exploratory factor analysis (EFA) demonstrated that each measure assessed its own cognitive construct regarding violence.Most factors from the EFA were significantly and incrementally related to violent outcomes.These findings will advance understanding of violent cognitions, their assessment, and role in violent behaviour.Keywords: implicit evaluative attitudes, explicit evaluative attitudes, beliefs regarding violence, violent cognitions, violent behaviour continued guidance, support, time, and effort he has invested in mentoring me throughout my doctorate, as well as the patience he has displayed in teaching me throughout the years that has provided me with the confidence to grow as a researcher.I met Kevin many years ago during my undergraduate degree when taking an introduction to forensic psychology course at Carleton, at which time I was an English major.That course inspired me to switch my major and pursue graduate studies in forensic psychology.Years later he took a chance on me, when few others did, by accepting me for my M.A.studies and later for my Ph.D.As such, I will forever be indebted to Kevin for where I am today and for all I have been able to accomplish over the last several years.He has taught me to think critically and provided me with invaluable skills in regard to teaching, managing research
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".